![]() ![]() If you share it inadvertantly, just get on Google’s website and regenerate your key - this will retire the old one. Your API key is private and unique to you, so be careful not to share it online, for example in a GitHub issue or saving it in a shared R script file. If you use the former, know that you’ll need to re-do it every time you reset R. This can be done on a temporary basis with register_google(key = "") or permanently using register_google(key = "", write = TRUE) (note: this will overwrite your ~/.Renviron file by replacing/adding the relevant line). Inside R, after loading the new version of ggmap, you’ll need provide ggmap with your API key, a hash value (think string of jibberish) that authenticates you to Google’s servers. Enabling the APIs just means clicking a few radio buttons on the Google Maps Platform web interface listed above, so it’s easy. You’ll only need to do that once, and then they’ll be ready for you to use. However, you do need to enable the APIs before you use them. For example, you just need to remember that get_googlemap() gets maps, geocode() geocodes (with Google, DSK is done), etc., and ggmap handles the queries for you. Apart from the relevant Terms of Service, generally ggmap users don’t need to think about the different services. For example, the Maps Static API provides map images, while the Geocoding API provides geocoding and reverse geocoding services. What may appear to ggmap users as one overarching “Google Maps” product, Google in fact has several services that it provides as geo-related solutions. Users must enable the APIs they intend to use. While it will require a valid credit card (sorry!), there seems to be a fair bit of free use before you incur charges, and even then the charges are modest for light use. From a user’s perspective, there are essentially three ramifications of this: 2.84 driving Google Maps API keyĪ few years ago Google has changed its API requirements, and ggmap users are now required to register with Google. Mapdist( c( "houston, texas", "dallas"), "waco, texas") # ℹ # ℹ # A tibble: 2 × 9 # from to m km miles seconds minutes hours mode # 1 dallas waco, texas 155265 155. Map distances, in both length and anticipated time, can be computed with mapdist()). (They also provide information on how long it takes to get from point A to point B.) # Call `lifecycle::last_lifecycle_warnings()` to see where this warning was # generated. ![]() # This warning is displayed once every 8 hours. 5, data = trek_df, lineend = "round" ) # ℹ # ℹ # Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. Trek_df qmap( "college station, texas", zoom = 8) + geom_path( aes( x = lon, y = lat), colour = "blue", size = 1.5, alpha =. There is also a mutate_geocode() that works similarly to dplyr’s mutate() function: Rundle, Bader, and Moody (2022) have considered this issue and suggest various alternative options for such data. Please take care not to disclose sensitive information. Note: geocode() uses Google’s Geocoding API to geocode addresses. Geocode( "1301 S University Parks Dr, Waco, TX 76798") # ℹ # A tibble: 1 × 2 # lon lat # 1 -97.1 31.6 revgeocode( c( lon = -97.1161, lat = 31.55098)) # ℹ # Warning: Multiple addresses found, the first will be returned: # ! 1301 S University Parks Dr, Waco, TX 76706, USA # ! 55 Baylor Ave, Waco, TX 76706, USA # ! HV2M+9H Waco, TX, USA # ! Bear Trail, Waco, TX 76706, USA # ! Robinson, TX 76706, USA # ! Waco, TX, USA # ! McLennan County, TX, USA # ! Texas, USA # ! United States # "1301 S University Parks Dr, Waco, TX 76706, USA" In that workflow you get the map first (and you can visualize it with ggmap()): Nevertheless, doing it yourself is more efficient. Often qmplot() is easiest because it automatically computes a nice bounding box for you without having to pre-compute it for yourself, get a map, and then use ggmap(map) in place of where you would ordinarily (in a ggplot2 formulation) use ggplot(). Library( "dplyr", nflicts = FALSE) library( "forcats") # define helper ` %notin% ` filter( offense %notin% c( "auto theft", "theft", "burglary"), between(lon, -95.39681, -95.34188), between(lat, 29.73631, 29.78400) ) | > mutate( offense = fct_drop(offense), offense = fct_relevel(offense, c( "robbery", "aggravated assault", "rape", "murder")) ) # use qmplot to make a scatterplot on a map qmplot(lon, lat, data = violent_crimes, maptype = "stamen_toner_lite", color = I( "red")) # ℹ Using `zoom = 14` # ℹ © Stadia Maps © Stamen Design © OpenMapTiles © OpenStreetMap contributors. ![]()
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